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Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules, 2020
Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann · 2011
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Klicpera, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann · 2011
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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
A.P. Thompson, L.P. Swiler, C.R. Trott, S.M. Foiles, and G.J. Tucker · 2014
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Group equivariant convolutional networks, 2016
Taco S. Cohen and Max Welling · 2016
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Semi-supervised classification with graph convolutional networks, 2016
Thomas N. Kipf and Max Welling · 2016
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Moment tensor potentials: A class of systematically improvable interatomic potentials
Alexander V. Shapeev · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 2017
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Neural message passing for quantum chemistry, 2017
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Deep learning scaling is predictable, empirically, 2017
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Data-driven learning of total and local energies in elemental boron
Volker L Deringer, Chris J Pickard, and Gábor Csányi · 2018
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Alchemical and structural distribution based representation for universal quantum machine learning
Felix A. Faber, Anders S. Christensen, Bing Huang, and O. Anatole von Lilienfeld · 2018
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N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials
Risi Kondor · 2018
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Risi Kondor and Shubhendu Trivedi · 2018
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Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers, Justin S Smith, and Kipton Barros · 2018
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges, 2021
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Gaussian process regression for materials and molecules
Volker L Deringer, Albert P Bartók, Noam Bernstein, David M Wilkins, Michele Ceriotti, and Gábor Csányi · 2021
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Gemnet: Universal directional graph neural networks for molecules
Johannes Klicpera, Florian Becker, and Stephan Günnemann · 2021
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Linear atomic cluster expansion force fields for organic molecules: Beyond rmse
Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, and Gábor Csányi · 2021
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Spherical message passing for 3d graph networks, 2021
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven M. Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
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Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
Michael J Willatt, Félix Musil, and Michele Ceriotti · 2018
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Graph neural networks: A review of methods and applications, 2018
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor · 2019
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Atomic cluster expansion for accurate and transferable interatomic potentials
Ralf Drautz · 2019
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Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
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E(n) equivariant graph neural networks, 2021
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T. Schütt, Oliver T. Unke, and Michael Gastegger · 2021
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The design space of E(3)-equivariant atom-centered interatomic potentials, 2022
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, and Gábor Csányi · 2022
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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Multilayer atomic cluster expansion for semi-local interactions, 2022
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Atomic cluster expansion: Completeness, efficiency and stability
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e3nn: Euclidean neural networks, 2022
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Learning local equivariant representations for large-scale atomistic dynamics, 2022
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Incompleteness of graph convolutional neural networks for points clouds in three dimensions
Sergey N Pozdnyakov and Michele Ceriotti · 2022
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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